Sagban, Rafid and Ku-Mahamud, Ku Ruhana and Abu Bakar, Muhamad Shahbani (2017) Unified strategy for intensification and diversification balance in ACO metaheuristic. In: 8th International Conference on Information Technology (ICIT), 17-18 May 2017, Amman, Jordan.

PDF Restricted to Registered users only Download (364kB) | Request a copy

Abstract

This intensification and diversification in Ant Colony Optimization (ACO) is the search strategy to achieve a trade-off between learning a new search experience (exploration) and earning from the previous experience (exploitation).The automation between the two processes is maintained using reactive search. However, existing works in ACO were limited either to the management of pheromone memory or to the adaptation of few parameters.This paper introduces the reactive ant colony optimization (RACO) strategy that sticks to the reactive way of automation using memory, diversity indication, and parameterization. The performance of RACO is evaluated on the travelling salesman and quadratic assignment problems from TSPLIB and QAPLIB, respectively.Results based on a comparison of relative percentage deviation revealed the superiority of RACO over other well-known metaheuristics algorithms.The output of this study can improve the quality of solutions as exemplified by RACO.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	INSPEC Accession Number: 17285610
Uncontrolled Keywords:	— Metaheuristics; Ant Colony Optimization; Reactive Heuristics; Recursive Local Search; Reward Assignment Strategies
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	School of Computing
Depositing User:	Mrs. Norazmilah Yaakub
Date Deposited:	02 Apr 2018 00:25
Last Modified:	02 Apr 2018 00:25
URI:	https://repo.uum.edu.my/id/eprint/23773

Actions (login required)

View Item